Zomato EDA.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zomato Dataset EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Zomato Dataset EDA\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9546</th>\n",
       "      <td>5915730</td>\n",
       "      <td>NamlÛ± Gurme</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...</td>\n",
       "      <td>Karakí_y</td>\n",
       "      <td>Karakí_y, ÛÁstanbul</td>\n",
       "      <td>28.977392</td>\n",
       "      <td>41.022793</td>\n",
       "      <td>Turkish</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9547</th>\n",
       "      <td>5908749</td>\n",
       "      <td>Ceviz AÛôacÛ±</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...</td>\n",
       "      <td>Koôuyolu</td>\n",
       "      <td>Koôuyolu, ÛÁstanbul</td>\n",
       "      <td>29.041297</td>\n",
       "      <td>41.009847</td>\n",
       "      <td>World Cuisine, Patisserie, Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9548</th>\n",
       "      <td>5915807</td>\n",
       "      <td>Huqqa</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.034640</td>\n",
       "      <td>41.055817</td>\n",
       "      <td>Italian, World Cuisine</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9549</th>\n",
       "      <td>5916112</td>\n",
       "      <td>Aôôk Kahve</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.036019</td>\n",
       "      <td>41.057979</td>\n",
       "      <td>Restaurant Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9550</th>\n",
       "      <td>5927402</td>\n",
       "      <td>Walter's Coffee Roastery</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21/B,...</td>\n",
       "      <td>Moda</td>\n",
       "      <td>Moda, ÛÁstanbul</td>\n",
       "      <td>29.026016</td>\n",
       "      <td>40.984776</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9551 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Restaurant ID           Restaurant Name  Country Code              City  \\\n",
       "0           6317637          Le Petit Souffle           162       Makati City   \n",
       "1           6304287          Izakaya Kikufuji           162       Makati City   \n",
       "2           6300002    Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3           6318506                      Ooma           162  Mandaluyong City   \n",
       "4           6314302               Sambo Kojin           162  Mandaluyong City   \n",
       "...             ...                       ...           ...               ...   \n",
       "9546        5915730              NamlÛ± Gurme           208         ÛÁstanbul   \n",
       "9547        5908749             Ceviz AÛôacÛ±           208         ÛÁstanbul   \n",
       "9548        5915807                     Huqqa           208         ÛÁstanbul   \n",
       "9549        5916112              Aôôk Kahve           208         ÛÁstanbul   \n",
       "9550        5927402  Walter's Coffee Roastery           208         ÛÁstanbul   \n",
       "\n",
       "                                                Address  \\\n",
       "0     Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1     Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2     Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3     Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4     Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "...                                                 ...   \n",
       "9546  Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...   \n",
       "9547  Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...   \n",
       "9548  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9549  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9550  CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21/B,...   \n",
       "\n",
       "                                        Locality  \\\n",
       "0      Century City Mall, Poblacion, Makati City   \n",
       "1     Little Tokyo, Legaspi Village, Makati City   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3         SM Megamall, Ortigas, Mandaluyong City   \n",
       "4         SM Megamall, Ortigas, Mandaluyong City   \n",
       "...                                          ...   \n",
       "9546                                    Karakí_y   \n",
       "9547                                   Koôuyolu   \n",
       "9548                                 Kuruí_eôme   \n",
       "9549                                 Kuruí_eôme   \n",
       "9550                                        Moda   \n",
       "\n",
       "                                       Locality Verbose   Longitude  \\\n",
       "0     Century City Mall, Poblacion, Makati City, Mak...  121.027535   \n",
       "1     Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831   \n",
       "3     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475   \n",
       "4     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508   \n",
       "...                                                 ...         ...   \n",
       "9546                                Karakí_y, ÛÁstanbul   28.977392   \n",
       "9547                               Koôuyolu, ÛÁstanbul   29.041297   \n",
       "9548                             Kuruí_eôme, ÛÁstanbul   29.034640   \n",
       "9549                             Kuruí_eôme, ÛÁstanbul   29.036019   \n",
       "9550                                    Moda, ÛÁstanbul   29.026016   \n",
       "\n",
       "       Latitude                          Cuisines  ...          Currency  \\\n",
       "0     14.565443        French, Japanese, Desserts  ...  Botswana Pula(P)   \n",
       "1     14.553708                          Japanese  ...  Botswana Pula(P)   \n",
       "2     14.581404  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)   \n",
       "3     14.585318                   Japanese, Sushi  ...  Botswana Pula(P)   \n",
       "4     14.584450                  Japanese, Korean  ...  Botswana Pula(P)   \n",
       "...         ...                               ...  ...               ...   \n",
       "9546  41.022793                           Turkish  ...  Turkish Lira(TL)   \n",
       "9547  41.009847   World Cuisine, Patisserie, Cafe  ...  Turkish Lira(TL)   \n",
       "9548  41.055817            Italian, World Cuisine  ...  Turkish Lira(TL)   \n",
       "9549  41.057979                   Restaurant Cafe  ...  Turkish Lira(TL)   \n",
       "9550  40.984776                              Cafe  ...  Turkish Lira(TL)   \n",
       "\n",
       "     Has Table booking Has Online delivery Is delivering now  \\\n",
       "0                  Yes                  No                No   \n",
       "1                  Yes                  No                No   \n",
       "2                  Yes                  No                No   \n",
       "3                   No                  No                No   \n",
       "4                  Yes                  No                No   \n",
       "...                ...                 ...               ...   \n",
       "9546                No                  No                No   \n",
       "9547                No                  No                No   \n",
       "9548                No                  No                No   \n",
       "9549                No                  No                No   \n",
       "9550                No                  No                No   \n",
       "\n",
       "     Switch to order menu Price range  Aggregate rating  Rating color  \\\n",
       "0                      No           3               4.8    Dark Green   \n",
       "1                      No           3               4.5    Dark Green   \n",
       "2                      No           4               4.4         Green   \n",
       "3                      No           4               4.9    Dark Green   \n",
       "4                      No           4               4.8    Dark Green   \n",
       "...                   ...         ...               ...           ...   \n",
       "9546                   No           3               4.1         Green   \n",
       "9547                   No           3               4.2         Green   \n",
       "9548                   No           4               3.7        Yellow   \n",
       "9549                   No           4               4.0         Green   \n",
       "9550                   No           2               4.0         Green   \n",
       "\n",
       "     Rating text Votes  \n",
       "0      Excellent   314  \n",
       "1      Excellent   591  \n",
       "2      Very Good   270  \n",
       "3      Excellent   365  \n",
       "4      Excellent   229  \n",
       "...          ...   ...  \n",
       "9546   Very Good   788  \n",
       "9547   Very Good  1034  \n",
       "9548        Good   661  \n",
       "9549   Very Good   901  \n",
       "9550   Very Good   591  \n",
       "\n",
       "[9551 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\tijo.thomas\\OneDrive - edelweisstokio.in\\Desktop\\Python\\Exploratory Data Analysis\\Zomatodataset\\zomato.csv\", encoding='latin-1')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9551 entries, 0 to 9550\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Restaurant ID         9551 non-null   int64  \n",
      " 1   Restaurant Name       9551 non-null   object \n",
      " 2   Country Code          9551 non-null   int64  \n",
      " 3   City                  9551 non-null   object \n",
      " 4   Address               9551 non-null   object \n",
      " 5   Locality              9551 non-null   object \n",
      " 6   Locality Verbose      9551 non-null   object \n",
      " 7   Longitude             9551 non-null   float64\n",
      " 8   Latitude              9551 non-null   float64\n",
      " 9   Cuisines              9542 non-null   object \n",
      " 10  Average Cost for two  9551 non-null   int64  \n",
      " 11  Currency              9551 non-null   object \n",
      " 12  Has Table booking     9551 non-null   object \n",
      " 13  Has Online delivery   9551 non-null   object \n",
      " 14  Is delivering now     9551 non-null   object \n",
      " 15  Switch to order menu  9551 non-null   object \n",
      " 16  Price range           9551 non-null   int64  \n",
      " 17  Aggregate rating      9551 non-null   float64\n",
      " 18  Rating color          9551 non-null   object \n",
      " 19  Rating text           9551 non-null   object \n",
      " 20  Votes                 9551 non-null   int64  \n",
      "dtypes: float64(3), int64(5), object(13)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.551000e+03</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "      <td>9551.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.051128e+06</td>\n",
       "      <td>18.365616</td>\n",
       "      <td>64.126574</td>\n",
       "      <td>25.854381</td>\n",
       "      <td>1199.210763</td>\n",
       "      <td>1.804837</td>\n",
       "      <td>2.666370</td>\n",
       "      <td>156.909748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.791521e+06</td>\n",
       "      <td>56.750546</td>\n",
       "      <td>41.467058</td>\n",
       "      <td>11.007935</td>\n",
       "      <td>16121.183073</td>\n",
       "      <td>0.905609</td>\n",
       "      <td>1.516378</td>\n",
       "      <td>430.169145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-157.948486</td>\n",
       "      <td>-41.330428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.019625e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.081343</td>\n",
       "      <td>28.478713</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.004089e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.191964</td>\n",
       "      <td>28.570469</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.835229e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>77.282006</td>\n",
       "      <td>28.642758</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>131.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.850065e+07</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>174.832089</td>\n",
       "      <td>55.976980</td>\n",
       "      <td>800000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>10934.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Restaurant ID  Country Code    Longitude     Latitude  \\\n",
       "count   9.551000e+03   9551.000000  9551.000000  9551.000000   \n",
       "mean    9.051128e+06     18.365616    64.126574    25.854381   \n",
       "std     8.791521e+06     56.750546    41.467058    11.007935   \n",
       "min     5.300000e+01      1.000000  -157.948486   -41.330428   \n",
       "25%     3.019625e+05      1.000000    77.081343    28.478713   \n",
       "50%     6.004089e+06      1.000000    77.191964    28.570469   \n",
       "75%     1.835229e+07      1.000000    77.282006    28.642758   \n",
       "max     1.850065e+07    216.000000   174.832089    55.976980   \n",
       "\n",
       "       Average Cost for two  Price range  Aggregate rating         Votes  \n",
       "count           9551.000000  9551.000000       9551.000000   9551.000000  \n",
       "mean            1199.210763     1.804837          2.666370    156.909748  \n",
       "std            16121.183073     0.905609          1.516378    430.169145  \n",
       "min                0.000000     1.000000          0.000000      0.000000  \n",
       "25%              250.000000     1.000000          2.500000      5.000000  \n",
       "50%              400.000000     2.000000          3.200000     31.000000  \n",
       "75%              700.000000     2.000000          3.700000    131.000000  \n",
       "max           800000.000000     4.000000          4.900000  10934.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Data analysis what all things we do\n",
    "1 . Missing Values\n",
    "2 . Explore about the numerical variables\n",
    "3 . Explore about categorical variables\n",
    "4 . Finding relationship betweeen features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Restaurant ID           0\n",
       "Restaurant Name         0\n",
       "Country Code            0\n",
       "City                    0\n",
       "Address                 0\n",
       "Locality                0\n",
       "Locality Verbose        0\n",
       "Longitude               0\n",
       "Latitude                0\n",
       "Cuisines                9\n",
       "Average Cost for two    0\n",
       "Currency                0\n",
       "Has Table booking       0\n",
       "Has Online delivery     0\n",
       "Is delivering now       0\n",
       "Switch to order menu    0\n",
       "Price range             0\n",
       "Aggregate rating        0\n",
       "Rating color            0\n",
       "Rating text             0\n",
       "Votes                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cuisines']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check no of null values in each features\n",
    "[features for features in df.columns if df[features].isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),yticklabels= False , cbar = False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94</td>\n",
       "      <td>Indonesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>148</td>\n",
       "      <td>New Zealand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>162</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>166</td>\n",
       "      <td>Qatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>184</td>\n",
       "      <td>Singapore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>189</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>191</td>\n",
       "      <td>Sri Lanka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>208</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>214</td>\n",
       "      <td>UAE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>215</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>216</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country Code         Country\n",
       "0              1           India\n",
       "1             14       Australia\n",
       "2             30          Brazil\n",
       "3             37          Canada\n",
       "4             94       Indonesia\n",
       "5            148     New Zealand\n",
       "6            162     Phillipines\n",
       "7            166           Qatar\n",
       "8            184       Singapore\n",
       "9            189    South Africa\n",
       "10           191       Sri Lanka\n",
       "11           208          Turkey\n",
       "12           214             UAE\n",
       "13           215  United Kingdom\n",
       "14           216   United States"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country = pd.read_excel(r\"C:\\Users\\tijo.thomas\\OneDrive - edelweisstokio.in\\Desktop\\Python\\Exploratory Data Analysis\\Zomatodataset\\Country-Code.xlsx\")\n",
    "df_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9546</th>\n",
       "      <td>5915730</td>\n",
       "      <td>NamlÛ± Gurme</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...</td>\n",
       "      <td>Karakí_y</td>\n",
       "      <td>Karakí_y, ÛÁstanbul</td>\n",
       "      <td>28.977392</td>\n",
       "      <td>41.022793</td>\n",
       "      <td>Turkish</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>788</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9547</th>\n",
       "      <td>5908749</td>\n",
       "      <td>Ceviz AÛôacÛ±</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...</td>\n",
       "      <td>Koôuyolu</td>\n",
       "      <td>Koôuyolu, ÛÁstanbul</td>\n",
       "      <td>29.041297</td>\n",
       "      <td>41.009847</td>\n",
       "      <td>World Cuisine, Patisserie, Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>1034</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9548</th>\n",
       "      <td>5915807</td>\n",
       "      <td>Huqqa</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.034640</td>\n",
       "      <td>41.055817</td>\n",
       "      <td>Italian, World Cuisine</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>661</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9549</th>\n",
       "      <td>5916112</td>\n",
       "      <td>Aôôk Kahve</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.036019</td>\n",
       "      <td>41.057979</td>\n",
       "      <td>Restaurant Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>901</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9550</th>\n",
       "      <td>5927402</td>\n",
       "      <td>Walter's Coffee Roastery</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21/B,...</td>\n",
       "      <td>Moda</td>\n",
       "      <td>Moda, ÛÁstanbul</td>\n",
       "      <td>29.026016</td>\n",
       "      <td>40.984776</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>591</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9551 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Restaurant ID           Restaurant Name  Country Code              City  \\\n",
       "0           6317637          Le Petit Souffle           162       Makati City   \n",
       "1           6304287          Izakaya Kikufuji           162       Makati City   \n",
       "2           6300002    Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3           6318506                      Ooma           162  Mandaluyong City   \n",
       "4           6314302               Sambo Kojin           162  Mandaluyong City   \n",
       "...             ...                       ...           ...               ...   \n",
       "9546        5915730              NamlÛ± Gurme           208         ÛÁstanbul   \n",
       "9547        5908749             Ceviz AÛôacÛ±           208         ÛÁstanbul   \n",
       "9548        5915807                     Huqqa           208         ÛÁstanbul   \n",
       "9549        5916112              Aôôk Kahve           208         ÛÁstanbul   \n",
       "9550        5927402  Walter's Coffee Roastery           208         ÛÁstanbul   \n",
       "\n",
       "                                                Address  \\\n",
       "0     Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1     Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2     Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3     Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4     Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "...                                                 ...   \n",
       "9546  Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...   \n",
       "9547  Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...   \n",
       "9548  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9549  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9550  CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21/B,...   \n",
       "\n",
       "                                        Locality  \\\n",
       "0      Century City Mall, Poblacion, Makati City   \n",
       "1     Little Tokyo, Legaspi Village, Makati City   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3         SM Megamall, Ortigas, Mandaluyong City   \n",
       "4         SM Megamall, Ortigas, Mandaluyong City   \n",
       "...                                          ...   \n",
       "9546                                    Karakí_y   \n",
       "9547                                   Koôuyolu   \n",
       "9548                                 Kuruí_eôme   \n",
       "9549                                 Kuruí_eôme   \n",
       "9550                                        Moda   \n",
       "\n",
       "                                       Locality Verbose   Longitude  \\\n",
       "0     Century City Mall, Poblacion, Makati City, Mak...  121.027535   \n",
       "1     Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831   \n",
       "3     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475   \n",
       "4     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508   \n",
       "...                                                 ...         ...   \n",
       "9546                                Karakí_y, ÛÁstanbul   28.977392   \n",
       "9547                               Koôuyolu, ÛÁstanbul   29.041297   \n",
       "9548                             Kuruí_eôme, ÛÁstanbul   29.034640   \n",
       "9549                             Kuruí_eôme, ÛÁstanbul   29.036019   \n",
       "9550                                    Moda, ÛÁstanbul   29.026016   \n",
       "\n",
       "       Latitude                          Cuisines  ...  Has Table booking  \\\n",
       "0     14.565443        French, Japanese, Desserts  ...                Yes   \n",
       "1     14.553708                          Japanese  ...                Yes   \n",
       "2     14.581404  Seafood, Asian, Filipino, Indian  ...                Yes   \n",
       "3     14.585318                   Japanese, Sushi  ...                 No   \n",
       "4     14.584450                  Japanese, Korean  ...                Yes   \n",
       "...         ...                               ...  ...                ...   \n",
       "9546  41.022793                           Turkish  ...                 No   \n",
       "9547  41.009847   World Cuisine, Patisserie, Cafe  ...                 No   \n",
       "9548  41.055817            Italian, World Cuisine  ...                 No   \n",
       "9549  41.057979                   Restaurant Cafe  ...                 No   \n",
       "9550  40.984776                              Cafe  ...                 No   \n",
       "\n",
       "     Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                     No                No                   No           3   \n",
       "1                     No                No                   No           3   \n",
       "2                     No                No                   No           4   \n",
       "3                     No                No                   No           4   \n",
       "4                     No                No                   No           4   \n",
       "...                  ...               ...                  ...         ...   \n",
       "9546                  No                No                   No           3   \n",
       "9547                  No                No                   No           3   \n",
       "9548                  No                No                   No           4   \n",
       "9549                  No                No                   No           4   \n",
       "9550                  No                No                   No           2   \n",
       "\n",
       "     Aggregate rating  Rating color  Rating text Votes      Country  \n",
       "0                 4.8    Dark Green    Excellent   314  Phillipines  \n",
       "1                 4.5    Dark Green    Excellent   591  Phillipines  \n",
       "2                 4.4         Green    Very Good   270  Phillipines  \n",
       "3                 4.9    Dark Green    Excellent   365  Phillipines  \n",
       "4                 4.8    Dark Green    Excellent   229  Phillipines  \n",
       "...               ...           ...          ...   ...          ...  \n",
       "9546              4.1         Green    Very Good   788       Turkey  \n",
       "9547              4.2         Green    Very Good  1034       Turkey  \n",
       "9548              3.7        Yellow         Good   661       Turkey  \n",
       "9549              4.0         Green    Very Good   901       Turkey  \n",
       "9550              4.0         Green    Very Good   591       Turkey  \n",
       "\n",
       "[9551 rows x 22 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.merge(df, df_country , on = 'Country Code', how ='left')\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Restaurant ID             int64\n",
       "Restaurant Name          object\n",
       "Country Code              int64\n",
       "City                     object\n",
       "Address                  object\n",
       "Locality                 object\n",
       "Locality Verbose         object\n",
       "Longitude               float64\n",
       "Latitude                float64\n",
       "Cuisines                 object\n",
       "Average Cost for two      int64\n",
       "Currency                 object\n",
       "Has Table booking        object\n",
       "Has Online delivery      object\n",
       "Is delivering now        object\n",
       "Switch to order menu     object\n",
       "Price range               int64\n",
       "Aggregate rating        float64\n",
       "Rating color             object\n",
       "Rating text              object\n",
       "Votes                     int64\n",
       "Country                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check data types\n",
    "final_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
       "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
       "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
       "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
       "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
       "       'Votes', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_names  = final_df.Country.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_val = final_df.Country.value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1e772797910>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7727b8e90>,\n",
       "  <matplotlib.patches.Wedge at 0x1e7727ba410>],\n",
       " [Text(-1.0829742700952103, 0.19278674827836725, 'India'),\n",
       "  Text(1.077281715838356, -0.22240527134123297, 'United States'),\n",
       "  Text(1.0995865153823035, -0.03015783794312073, 'United Kingdom')],\n",
       " [Text(-0.590713238233751, 0.10515640815183668, '94.39%'),\n",
       "  Text(0.5876082086391032, -0.12131196618612707, '4.73%'),\n",
       "  Text(0.5997744629358018, -0.01644972978715676, '0.87%')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##plotting Pie Chart\n",
    "plt.pie(country_val[:3], labels = country_names[:3] , autopct= '%1.2F%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Observation : Zomato maximum records are from India , Usa and UK respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = final_df.groupby(['Aggregate rating', 'Rating color', 'Rating text']).size().reset_index().rename(columns={0:'Ratings count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Ratings count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.2</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.3</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.4</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.6</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.7</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.8</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.9</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.1</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.2</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.3</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.4</td>\n",
       "      <td>Orange</td>\n",
       "      <td>Average</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.5</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.6</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.7</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.8</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.9</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.1</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.3</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.6</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.7</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Aggregate rating Rating color Rating text  Ratings count\n",
       "0                0.0        White   Not rated           2148\n",
       "1                1.8          Red        Poor              1\n",
       "2                1.9          Red        Poor              2\n",
       "3                2.0          Red        Poor              7\n",
       "4                2.1          Red        Poor             15\n",
       "5                2.2          Red        Poor             27\n",
       "6                2.3          Red        Poor             47\n",
       "7                2.4          Red        Poor             87\n",
       "8                2.5       Orange     Average            110\n",
       "9                2.6       Orange     Average            191\n",
       "10               2.7       Orange     Average            250\n",
       "11               2.8       Orange     Average            315\n",
       "12               2.9       Orange     Average            381\n",
       "13               3.0       Orange     Average            468\n",
       "14               3.1       Orange     Average            519\n",
       "15               3.2       Orange     Average            522\n",
       "16               3.3       Orange     Average            483\n",
       "17               3.4       Orange     Average            498\n",
       "18               3.5       Yellow        Good            480\n",
       "19               3.6       Yellow        Good            458\n",
       "20               3.7       Yellow        Good            427\n",
       "21               3.8       Yellow        Good            400\n",
       "22               3.9       Yellow        Good            335\n",
       "23               4.0        Green   Very Good            266\n",
       "24               4.1        Green   Very Good            274\n",
       "25               4.2        Green   Very Good            221\n",
       "26               4.3        Green   Very Good            174\n",
       "27               4.4        Green   Very Good            144\n",
       "28               4.5   Dark Green   Excellent             95\n",
       "29               4.6   Dark Green   Excellent             78\n",
       "30               4.7   Dark Green   Excellent             42\n",
       "31               4.8   Dark Green   Excellent             25\n",
       "32               4.9   Dark Green   Excellent             61"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Observations\n",
    "1 . When rating is from 4.5 to 4.9 ---> Excellent\n",
    "2 . When rating is from 4. to 3.4 ---> Very Good\n",
    "3 . When rating is from 3.5 to 3.9 ---> Good\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Ratings count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Not rated</td>\n",
       "      <td>2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.8</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1</td>\n",
       "      <td>Red</td>\n",
       "      <td>Poor</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aggregate rating Rating color Rating text  Ratings count\n",
       "0               0.0        White   Not rated           2148\n",
       "1               1.8          Red        Poor              1\n",
       "2               1.9          Red        Poor              2\n",
       "3               2.0          Red        Poor              7\n",
       "4               2.1          Red        Poor             15"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Aggregate rating', ylabel='Ratings count'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,3)\n",
    "sns.barplot(x='Aggregate rating' , y='Ratings count', data = ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Aggregate rating', ylabel='Ratings count'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Aggregate rating' , y='Ratings count', data = ratings , hue = 'Rating color' , palette = ['white','red','orange','yellow','green' ,'blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Observation\n",
    "1. Not rated count is very high\n",
    "2. Maximum number of ratings are from 2.5 to 3.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Rating color', ylabel='count'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Count Plot\n",
    "#Frequency of colors\n",
    "sns.countplot(x = 'Rating color', data = ratings ,  palette = ['white','red','orange','yellow','green' ,'blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country     0\n",
       "0          Brazil     5\n",
       "1           India  2139\n",
       "2  United Kingdom     1\n",
       "3   United States     3"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the countries that has given 0 ratings\n",
    "final_df[final_df['Rating color']=='White'].groupby('Country').size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Country</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>India</td>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.8</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>4.9</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>4.9</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>4.9</td>\n",
       "      <td>UAE</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>4.9</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>4.9</td>\n",
       "      <td>United States</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Aggregate rating         Country     0\n",
       "0                 0.0          Brazil     5\n",
       "1                 0.0           India  2139\n",
       "2                 0.0  United Kingdom     1\n",
       "3                 0.0   United States     3\n",
       "4                 1.8           India     1\n",
       "..                ...             ...   ...\n",
       "217               4.9       Sri Lanka     1\n",
       "218               4.9          Turkey     3\n",
       "219               4.9             UAE     4\n",
       "220               4.9  United Kingdom     4\n",
       "221               4.9   United States    14\n",
       "\n",
       "[222 rows x 3 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.groupby(['Aggregate rating','Country']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Observatons \n",
    "maximum number of o ratings are from India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Currency</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazilian Real(R$)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>Indian Rupees(Rs.)</td>\n",
       "      <td>8652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Indonesian Rupiah(IDR)</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>NewZealand($)</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Phillipines</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>Qatari Rial(QR)</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Rand(R)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Sri Lankan Rupee(LKR)</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>UAE</td>\n",
       "      <td>Emirati Diram(AED)</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Pounds(£)</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>United States</td>\n",
       "      <td>Dollar($)</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country                Currency     0\n",
       "0        Australia               Dollar($)    24\n",
       "1           Brazil      Brazilian Real(R$)    60\n",
       "2           Canada               Dollar($)     4\n",
       "3            India      Indian Rupees(Rs.)  8652\n",
       "4        Indonesia  Indonesian Rupiah(IDR)    21\n",
       "5      New Zealand           NewZealand($)    40\n",
       "6      Phillipines        Botswana Pula(P)    22\n",
       "7            Qatar         Qatari Rial(QR)    20\n",
       "8        Singapore               Dollar($)    20\n",
       "9     South Africa                 Rand(R)    60\n",
       "10       Sri Lanka   Sri Lankan Rupee(LKR)    20\n",
       "11          Turkey        Turkish Lira(TL)    34\n",
       "12             UAE      Emirati Diram(AED)    60\n",
       "13  United Kingdom              Pounds(£)    80\n",
       "14   United States               Dollar($)   434"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Find out which currency is used by which country\n",
    "final_df.groupby(['Country','Currency']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>No</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>No</td>\n",
       "      <td>6229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>No</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>No</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Phillipines</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>No</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>UAE</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UAE</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country Has Online delivery     0\n",
       "0        Australia                  No    24\n",
       "1           Brazil                  No    60\n",
       "2           Canada                  No     4\n",
       "3            India                  No  6229\n",
       "4            India                 Yes  2423\n",
       "5        Indonesia                  No    21\n",
       "6      New Zealand                  No    40\n",
       "7      Phillipines                  No    22\n",
       "8            Qatar                  No    20\n",
       "9        Singapore                  No    20\n",
       "10    South Africa                  No    60\n",
       "11       Sri Lanka                  No    20\n",
       "12          Turkey                  No    34\n",
       "13             UAE                  No    32\n",
       "14             UAE                 Yes    28\n",
       "15  United Kingdom                  No    80\n",
       "16   United States                  No   434"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Which countries do have online deliveries option\n",
    "final_df.groupby(['Country','Has Online delivery']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Observations\n",
    "USA and India has online delivery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['New Delhi', 'Gurgaon', 'Noida', 'Faridabad', 'Ghaziabad',\n",
       "       'Bhubaneshwar', 'Amritsar', 'Ahmedabad', 'Lucknow', 'Guwahati',\n",
       "       ...\n",
       "       'Ojo Caliente', 'Montville', 'Monroe', 'Miller', 'Middleton Beach',\n",
       "       'Panchkula', 'Mc Millan', 'Mayfield', 'Macedon', 'Vineland Station'],\n",
       "      dtype='object', length=141)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Create a pie chart for cities distribution\n",
    "final_df.City.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_values = final_df.City.value_counts().values\n",
    "city_lables = final_df.City.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1e77668bcd0>,\n",
       "  <matplotlib.patches.Wedge at 0x1e776983150>,\n",
       "  <matplotlib.patches.Wedge at 0x1e776998250>,\n",
       "  <matplotlib.patches.Wedge at 0x1e776999050>,\n",
       "  <matplotlib.patches.Wedge at 0x1e776999e90>],\n",
       " [Text(-0.6145352824185932, 0.9123301960708633, 'New Delhi'),\n",
       "  Text(0.0623675251198054, -1.0982305276263407, 'Gurgaon'),\n",
       "  Text(0.8789045225625368, -0.6614581167535246, 'Noida'),\n",
       "  Text(1.0922218418223437, -0.13058119407559224, 'Faridabad'),\n",
       "  Text(1.099946280005612, -0.010871113182029924, 'Ghaziabad')])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OF6/myvVwjVhkGCZn5GtdghC1wqzKL3utWGQYyptFWLOruYVal9AgWWYYSstQWLGrORKGWrC4MMzMKyRfrqkIKyYtQ21YXBjmFhRrXYIQtSq9lsJQURQ2btxYK8cGCA8PR1EU0tPTK/2c/v37M378+HpRi8WFobQKhbW7lZZhUlIS48aNw9/fHzs7O7y8vAgNDWXZsmXk5NTNcJ1evXpx8eJFGjVqVCfnq2k2WhdQVflF0jIU1i0zr2phGBcXR2hoKC4uLsydO5egoCBsbW05ceIEy5cvp1mzZjzwwAO1VO0fjEYj3t7etX6e2mJxLcO8QmkZCutWXMW3+OjRo7GxseHgwYMMGzaMtm3b4ufnx4MPPsjmzZsZMmRI6b6pqakMHToUBwcHAgIC2LRp0x/nLS5m5MiRtGzZEnt7ewIDA1m8eHGZcymKct2Xr68vcP1H08uXLzN8+HCaNWuGg4MDQUFBfPXVV9fVX1RUxJgxY2jUqBEeHh7MnDkT9U93evvPf/5D165dcXJywtvbm8cff5yUlJQyx/j+++9p3bo19vb2DBgwgPj4+Kq9iFhgGMrHZGHtzFW45ePly5fZsmULL774IiaTqdx9FEUpffzaa68xbNgwjh8/zuDBgxkxYgRXrpTcS8hsNtO8eXPWrVtHREQE//znP5k2bRpr164tff7FixdLv06fPo2/vz99+/Yt97x5eXl06dKFzZs389tvvzFq1Cj+7//+j/3795fZb+XKldjY2LB//34WL17M22+/zSeffFL688LCQubMmcOxY8fYuHEj8fHxhIWFlf48MTGRhx9+mCFDhnD06FGeffZZpk6dWunXsPR1UlXLutnm3rjLPLZ8r9ZlNChPtY3BIxW8U4tR0GPW6THrDKg6PWadDarOBrNOX/K9oi/5XtGjln7pUNFjVhRQ9Kjorm1TUNHBtT8VLOqtWGv82rpz/91+ldp337599OjRg//+978MHTq0dLuHhwd5eSXjcV988UUWLFiAoijMmDGDOXPmAJCdnY2joyM//PAD99xzT7nHHzNmDElJSXz99ddltquqyiOPPEJCQgI7d+7E3t6e8PBwBgwYQFpaGi4uLuUe7/7776dNmza89dZbQEkHSkpKCidPniwN7alTp7Jp0yYiIiLKPcbBgwfp1q0bmZmZODo6Mm3aNL755htOnjxZus/UqVNZsGDBTWv5K4u7ZmijUyreSdSYp5sm0k+/hbEeiTi52TIg24euqU7clpCH6dQ5zKk1d4dCs40RjLZgsEU12ILBiNlgBBtbVBsj2BhQbQyoNsaSL70Nqt4AegPma49VvQH1WkCrej1m5dpj3R/BbFb+/FiHig7zta/fH6uqghkFs0rJY/XaYzMlj81gNqslfxarJY+LS76q27xwaeZS7ddy//79mM1mRowYQX7+H+NyO3bsWPrYZDLh7Oxc5iPn+++/z6effkpCQgK5ubkUFBQQHBx83fGnTZvGnj17OHjwIPb29uXWUFxczNy5c1m7di3nz5+noKCA/Px8HBwcyuzXo0ePMq3Xnj17smjRIoqLi9Hr9Rw6dIh//etfHDt2jLS0NMzmkk+HCQkJtGvXjsjISLp3717mmD179qz8i3WNxYWhnUGvdQkNyrP24TQ7vYsuwXdy6GoMm5xi2OQEtAT6QYfCZvRN96LdRT2ecVdQYs5CUdEtnUtXVABFBcAft3SwxH9tVacHgxHVYItqtC0Jc4MRfg9xgwHVxrYk2PWGa9sNmK8Fe+OCYCCgUufy9/dHURSio6PLbPfzK2lZ/jWoDAZDme8VRSkNl9WrVzNp0iQWLVpEz549cXJyYuHChezbt6/Mc1atWsU777xDeHg4zZo1u2FtCxcuZPHixbz77rsEBQVhMpkYP348BQUFlfq7QUnrddCgQQwaNIgvvvgCT09PEhISGDRoUJWOUxkShuKGAky5NE36CYCJqZd43HD9Pr8ZkvnNMxk8gY7gZLZlQE4AXS454ZuYjyn6HObUy3VbuMYUczHk56Lk597S8x06uFZ6X3d3d+666y7ee+89xo4de8PrhpWxa9cuevXqxejRo0u3xcbGltlnz549PPvss3z00Uf06NGjwuM9+OCDPPHEE0DJNclTp07Rrl27Mvv9NWz37t1LQEAAer2eqKgoLl++zPz582nRogVQ8jH5z9q2bVumI+j3Y1SVxXWg2BksrmSLNb3pIZTikt++QeeOc7dr+wqfk6nLZ5NjDLNaHuapvif52z+u8trEpoQ/25nk+7qhtvUHG4v7HVyndHZ2Vdr/gw8+oKioiK5du7JmzRoiIyOJjo5m1apVREVFoddXrgEREBDAwYMH+fHHHzl16hQzZ87kwIEDpT9PSkpi6NChPPbYYwwaNIikpCSSkpK4dOnSDY+3detWdu/eTWRkJM899xzJycnX7ZeQkMCECROIjo7mq6++YunSpYwbNw4AHx8fjEYjS5cuJS4ujk2bNpVe8/zd888/T0xMDJMnTyY6Opovv/ySFStWVPLV+4PFvSulZVg3FEUl9OrmMtvGJUTxcyMbisxV+xh80pjCSc+U0tajo2qkf7Y/3S45c9u5fEzR51EvpdZg9ZZNsa1aGLZq1YojR44wd+5cXn31Vc6dO4etrS3t2rVj0qRJZVp6N/Pcc89x5MgRHn30URRFYfjw4YwePZoffvgBgKioKJKTk1m5ciUrV64sfd5tt91W7lCWGTNmEBcXx6BBg3BwcGDUqFE89NBDXL1a9v7QTz75JLm5uYSEhKDX6xk3bhyjRo0CwNPTkxUrVjBt2jSWLFlC586deeutt8qMm/Tx8WH9+vW8/PLLLF26lJCQEObOncszzzxTpdfR4nqTs/KL6DDrR63LsHqjm8fzSuq067a/cft9rE4/UePna1foSd90b9on2eAZl4Yu5iwUNsw5uk1en4PL3/5W5+cNCwsjPT39plP2+vfvT3BwMO+++26ljlmZHuZbVdVaKmJxLUOTUY+NTqFI7i9bq54y/lzu9hdO7ePbph5kF9XsFK8IwyUiPC+VtB6DwMFsoH+OH90uN6JlQgGmU+dQUxpG61FfydAICwsr00L7XUxMDP7+/lU+7+LFi7GwtlGNsrgwVBQFd0ejLPBai4KcsmmcFF7uz9yyUwlzCOH9jN9qtYYcXSHfO8byvSNwG9AH2hR60y+9Ce2TbGh8Jh3dqXirbD3aeHlVet977rmHzz77rMw2T0/PKp2vuLgYRVEsdk5xTbHI3ghPJ1utS7Bqr3ofQLnJdcGnIsPxtHOrw4pKRBlS+cjzBC8FHeGxB84QNsGGz15qzcnHQ8juE4ziVbUQqK9sGjeu9L62trZ4e3uX+Vq8eHHpUJYWLVowevRosrL+uKPkihUrcHFxYdOmTbRr1w5bW1sSEhIICwvjoYceKt0vOzubJ598EkdHR5o0acKiRYuuO39lpspBSc9yx44dsbOzo0ePHvz22x+/TCszba8ytVSXRYaht3PVLjCLyjPoVLqnfXfTfewLcnhBp33w5OgK+cEUx2u3Hebp3r/x92fSmDnJm23/CObikG6Y2wegGI1al1k1Oh02Hh7VPISOJUuWcPLkSVauXMnPP//MK6+8UmafnJwcFixYwCeffMLJkydpXE4AT548mR07dvDNN9+wZcsWwsPDOXz4cJl9Kpoq9+djLVq0iAMHDuDp6cmQIUMovNaqr8y0vcrUUl0W9zEZoEmj8ke8i+p7qXks+pQLFe73cMTPrOrQnbisc3VQVeVFG1KJ9kgFD6AD2Kk2DMgJpFuqMy3PFeIYfQE1+fqWS31h4+GBUsmhMADfffcdjo6Opd/fe++9rFu3rvR7X19fXn/9dZ5//nk++OCD0u2FhYV88MEHdOrUqdzjZmVl8e9//5tVq1Zx5513AiVziJs3b15mvz/32Pr5+bFkyRK6detGVlZWmbpmzZrFXXfdVeY4GzZsYNiwYTRr1oxJkyaV7jt27Fh+/PFH1q5dS0hISKVrqS6LDMOmLhKGteVx/U+V2k+vFjMuT8+4Wq6nuvKUIn4wxfKDiZJrj6EQUORF/6tN6ZBkwCsuHf2peNQans1wqwzXBhZX1oABA1i2bFnp9yaTiW3btjFv3jyioqLIyMigqKiIvLw8cnJySqfCGY3GMlPz/io2NpaCgoIy09zc3NwIDAwss19FU+V+9+fpcb8fJzIyEqh42l5la6kuiwzD29wdKt5JVFnXRpm4Je2s9P53xOykc/AdHL56uharqnkxNpeJcb8M7kD7ktZjv5zWhFx2wS+xEMdT51GTtGk9GqsYhiaTqUzPcXx8PPfffz8vvPACb7zxBm5ubvz666+MHDmSgoKC0oCxt7cvMx/4VtTUVLmamLZXEywyDFt7OWldglV6tfFelMSqLZH28uUr/J9Fvov+kKcU8aMpjh9NgA8lrcdCL/plNKFDkhHvM+noT51Fza/9EQwGn6qF4V8dOnQIs9nMokWL0OlKugT+vARXZbVq1QqDwcC+ffvw8fEBIC0tjVOnTtGvXz+ASk2V+93evXuvO07btm2BiqftVaaWmmCRb+OWHiaMNjq52XYNstcXc/vlm3eclCc48SgDO9/DtrTyl1uyVDGGsq1HW1VPv5zWdL/iQsvEQpxOXUC9eP3UsuoytvCp1vP9/f0pLCxk6dKlDBkyhF27dvHhhx9W+TiOjo6MHDmSyZMn4+7uTuPGjZk+fXppwELZqXLPP/88v/3223VT5X43e/Zs3N3d8fLyYvr06Xh4eJT2XAcEBPD111+ze/duXF1defvtt0lOTi4Nw8rUUhMsMgz1OoVWno5EXszQuhSrMaFFDLqk8ueYVmRcYgzhTjYUqbe2Wo0lyFeK2WKKY4sJaAH0glZFjemf0ZSgJCNecVexORVf7dajbUDVB0v/WadOnXj77bdZsGABr776Kn379mXevHk8+eSTVT7WwoULycrKYsiQITg5OTFx4sQyU+kqM1Xud/Pnz2fcuHHExMQQHBzMt99+i/FaT39lpu1VVEtNsLjpeL8bv/oIG49W3OspKueo71Jckvbc8vNfv/0+1tTCND1LYqvq6ZPrQ4/LLvglFuEUcxH1QlKln6/Y2RF48ACKLGShCYt91QO9nQEJw5rQ1y2dRknVWz38hZj9fNvEnZwanqZnSfKVYrY5nGGbA6WtR78iT/pfbUZQshHvM1exOXUW9doK1H9l16aNBKGGLPaVb9fUWesSrMYk990oOdX7gOCedYkwUwgfXG3YrcO/irNJI849reTaYzswDtbTJ8+fHqmu+J0rwjkmCfX8RQDsOnTQttgGzmI/JmflF9HptS0Uy4IN1WKyKeaE0zh0udVfvj/H1pH7WvqTml9ztwJoCHyLXBiQ0YwhPZ8moMcgrctpsCxyOh6Ao60N7aV1WG1TW0TWSBACOORn8YJN5efVihLxNul85nYSB2kZaspiwxAgxLfuFwuwNg8Vb6nR4z0c8TO+phvfF0OUz9vkTTNHed20ZNFh2N3PXesSLNrdHldwSil/kOytsjEXMT7fYi9Fa6Zz485al9DgWXQYhvi6Uc0ZRQ3aBNfKT72rijtjdhLs3KpWjm2tunh10bqEBs+iw7CRg4F2TeS64a1wNRQRmPJDrR1/4pX0Wju2NerR5OZ3mhO1z6LDEOCudpVfFVj8YVqL31Dya28GT3DiEe5wbVfxjoI2bm3wca7eNDxRfRYfhvd08Na6BIt0X8H/av0c4xNjsFHk+mFFBvnKcJr6wOLDsI23syzpVUUPeaXgkHq81s/T8lIsQ13a1vp5LJ2EYf1g8WEIMKi9tA6rYoxz7XSclGd0zEHsbWQx3htp596OFk7VW7ZL1AwrCUO5blhZ3rYFtEqq/Y/Iv/PITOYpU/VWYrFm0iqsP6wiDDv7uNKkkdwkqjKmtTiOUphdp+d8OnIHbraudXpOSyFhWH9YRRgqisLfu9TszWGs1d05tTec5kZKpulJ6/2vgjyCZNZJPWIVYQgwrFsLdDIA+6aGN7mA3ZVITc79t4ifuc3UVJNz11fSKqxfrCYMm7s6EOpfvfvNWrvnTb9odm4bcxEvFVjYPYxrkU7RSRjWM1YThgDDQ2Tg6o00t8vHJ6lmF2WoqrtP/UJHmaYHwJ0+d+JtklEQ9YlVheFd7bxwN0nrozwzmx9BKSp/heW6NCFN7lsDMLLDSK1LEH9hVWFo0OukdXgDA7I2a10CAF0SDtHfpWFP0+vm3Y32Hu21LkP8hVWFIcDTob7YGazur1UtzzRLxJgeq3UZpV4+H4te0WtdhmbC2odpXYIoh9WlhrujLY91k9bhn420C9e6hDL8UmJ4qIG2DgNcA+jbvK/WZYhyWF0YAvyjrx8GvYyzAQgw5dI06Sety7jOi6cPYa9veAPlpVVYf1llGDZzseeBTjKYFWB600MoxQVal3Edz4wknnAM0LqMOuVt8ubelvdqXYa4AasMQ4AX+vs1+EHYiqLS++p3WpdxQyMjf8HN1kXrMurME22fwKAzaF2GuAGrDUP/xk483LlhT9Eb3fwsNhkJWpdxQ6b8TEYZmmhdRp1wsXXh763/rnUZ4iasNgwBJg8KxN7QcHstnzT8rHUJFRp28md8HKw/EF/q/BIOBll3sz6z6jD0crbjH339tC5DE0FO2TROCte6jAoZzIWMLbTujpT27u15JOARrcsQFbDqMAR4vp8fjZ1stS6jzr3qfQDFXKR1GZVyz6kdBDlb5y8tBYXp3aejU6z+v5rFs/p/IQejDRPvbq11GXXKoFPpnlZ/O07K83J6ltYl1IqHAx4myDNI6zJEJVh9GAL8vUsLOjVvpHUZdWZc81j0WRe0LqNKup09SF8ru1+Ks9GZcZ3HaV2GqKQGEYY6ncKCv3VsMAOxh+vr3yDrynj5QrxVTdMbe/tYXO1khW9L0SDCEEruovdCf+u/F0fXRpm4JdXdDZ9qkn9yNA9YyTS9tm5tGRY4TOsyRBU0mDAEGDPAn9ZejlqXUatebbwXRTVrXcYtezH2CHZ6y+7wUlCY1n2adJpYmAb1r2W00bHgkY5WOzPFXl/M7Zctq+Pkr7yuXuAJR8vu8Hq87eMENw7WugxRRQ0qDAFu93HlH32scxjHpBYx6HIuaV1GtY2M2omr0TI7vAJdA5nQZYLWZYhb0ODCEGDi3YF0tMLe5UfYqnUJNcIxL4NRRstbaMPexp43+72JUS+rrVuiBhmGRhsdS4ffjqOtjdal1Ji+buk0StqrdRk15tGIn2nuYFn3CJnSbQp+jermU4evry/vvvvuTfdRFIWNGzfWST3WoEGGIcBt7ibmP2I9g2Enue9GQdW6jBpjKC7gpWLLmct7n999PNK64il3YWFhKIrC/Pnzy2zfuHEjilL5i9kHDhxg1KhRVa5T3FiDDUOA+zs2JayXr9ZlVJuTTREdUr/Xuowad0/UDto7t9S6jAq1dm3NrJ6zKr2/nZ0dCxYsIC0t7ZbP6enpiYOD5fyysAQNOgwBpt/XlhBfN63LqJapPlHocq9oXUaNU1CZcDVH6zJuysngxDv938Hexr7Szxk4cCDe3t7MmzfvhvusX7+e9u3bY2tri6+vL4sWLSrz879+TI6JiaFv377Y2dnRrl07tm69/vrxlClTaN26NQ4ODvj5+TFz5kwKCwsrXbe1a/BhaNDr+Oj/utDSw6R1KbfsgSJt74dcm0LiD9DbpY3WZZRLQeGN3m/g41y1e+7o9Xrmzp3L0qVLOXfu3HU/P3ToEMOGDeOxxx7jxIkT/Otf/2LmzJmsWLGi3OOZzWYefvhhjEYj+/bt48MPP2TKlCnX7efk5MSKFSuIiIhg8eLFfPzxx7zzzjtVqt2aNfgwBHA1Gfk0rBuuDpa3CvHdHldwSjmodRm16uULCfVyAPMr3V5hgM+AW3ru0KFDCQ4OZtas6z9ev/3229x5553MnDmT1q1bExYWxpgxY1i4cGG5x9q2bRtRUVF8/vnndOrUib59+zJ37tzr9psxYwa9evXC19eXIUOGMGnSJNauXXtL9Vuj+vcO00hLDxMfP9kVo41lvSQTXC1z6l1VtE6OYohL/brP8AudXuCJdk9U6xgLFixg5cqVREZGltkeGRlJaGhomW2hoaHExMRQXFx83XEiIyNp0aIFTZs2Ld3Ws2fP6/Zbs2YNoaGheHt74+joyIwZM0hIqL8rodc1y/qfX8u6+rqx6O+dqEKnnqbcjYUEplhfx0l5xsQdxbaeTNN7ou0TjA4eXe3j9O3bl0GDBvHqq6/WQFU3t2fPHkaMGMHgwYP57rvvOHLkCNOnT6egoP7dLEwr1jPQroYM6dSUlMx85nwXoXUpFXq1+UmUC5lal1EnvNPPM6LlfXyafkLTOh5s9SCvdHulxo43f/58goODCQwMLN3Wtm1bdu3aVWa/Xbt20bp1a/T661f1adu2LYmJiVy8eJEmTUpuobB3b9kxp7t37+a2225j+vTppdvOnj1bY38PayAtw3KM7N2S6YPr/9p6gwv+p3UJderZyF9x0XCa3kCfgbzW67UqjQesSFBQECNGjGDJkiWl2yZOnMhPP/3EnDlzOHXqFCtXruS9995j0qRJ5dc1cCCtW7fmqaee4tixY+zcubNM6AEEBASQkJDA6tWriY2NZcmSJWzYsKHG/h7WQMLwBv7R14+p99bPXkyAh7xScEg9rnUZdcop7yr/0GiaXs8mPXmz75vodTW/3uLs2bMxm/9Yaahz586sXbuW1atX06FDB/75z38ye/ZswsLCyn2+Tqdjw4YN5ObmEhISwrPPPssbb7xRZp8HHniAl19+mTFjxhAcHMzu3buZOXNmjf9dLJmiqqr1TFuoBe9vP83CH6O1LuM62wLW45+4Xusy6lyh3siQNh05n5NSZ+fs5NmJ5Xctl7vbWTlpGVbgxQH+TB4UWPGOdcjbtoBWSQ3rI/LvDMUFjC12qrPzhXiH8MHADyQIGwAJw0p4cYA/8x8OwqaeLIQ4vflxlMJsrcvQzOCocNo6+db6eR5o9QAf3vUhzkbnWj+X0J6EYSU9FuLDx092xcGo/T067sptGMNpbkRBZUJmXq2eY3TwaN7o/QYGneUNxBe3RsKwCga0acyaUT3xcNRuvNvwJhewuxKl2fnrix5n9hNaC9P0DDoDc3vP5YVOL9T4sUX9JmFYRUHNG7FhdC/8PLWZy/y86RdNzlsfvXzxXI1O03M2OvPRXR8xpNWQGjumsBwShreghZsD37wYyr0d6nbxUR/7PHySrHdRhqoKTIrg/hqaptfcsTmrBq+im3e3GjmesDwShrfIyc7Asie6MOO+tnXWsTKj2VGUotq9VmZpxpw5Xu1pel28uvDFfV/QslH9XztR1B4Jw2p6to8fq0f1wMu59q8j9s/aXOvnsDRN0hJ53OnW7qZn0BmY0GUCnw76FDc7y17TUlSfhGEN6OrrxuaX+tAnwKPWzvFMs0SM6bG1dnxLNjJqF87Gqo09bO3amq/u+4qnOzxdL5cHE3VP3gU1xMPRlv+M7M4bQzvUyo2mRtptr/FjWotGuen8w7ZFpfbVKTqe7vA0q+9bTaBb/RpML7Ql0/Fqwbm0HKauP8Gvp1Nr5HgBply2qM+jmGWJ9hsp0NsyJDCIC7k3nqbXzLEZb/R+gy5eXeqwMmEppGVYC5q7OrDq2ZprJc5oelCCsALG4nzGqDeeKTLUfyjrH1gvQShuSFqGtezi1Vzm/xDFN0cv3NLzFUUlxvNVbDJkReKKqCgM69ibqMw/1unza+TH5G6T6d2st4aVCUsgYVhHDp1NY/Z3ERxLTK/S815sEc/kS9NqpygrtLtld57jIi62LrzQ6QWGBQ7DRidrGIuKSRjWIVVV2XDkPAv+F0VyRn6lnrPP7994XfipliuzInpb1gz+J/cGPSkLLIgqkTDUQE5BEZ/vOcsnO+NIzbrxPSiCnLLZVPQ8inr9TYDEX+hsIPhx6DcFGjXXuhphgSQMNZRXWMyX+xL46JfYcluKXwVsp2fixxpUZkkU6PAIDJgG7q20LkZYMAnDeiC/qJh1B8/x4Y5YzqXlAmDQqUS5TUKfdVHj6uopeze4fQR0eVpCUNQICcN6pNissi0ymc/3xNOzcB9jkv+pdUn1T4vu0HUktH8IbOrHrUOFdZAwrKcKU89gOPIZHFsNWclal6MtoxN0HAZdnwHvDlpXI6yUhGF9V1wEMVvgyCqI+RHMRVpXVHe8O0LXpyFoGNg6al2NsHIShpYk7yrEhcPpbXD6J8g4r3VFNcvoBH79wH9gyZdL5eYbC1ETJAwtWUpkSTDGbIWEvVBcubGL9UrjdiXBF3AX+PQEvdxzRGhDwtBaFGTDmZ0l4Zi4D1JjoChX66rKUvTg5gde7cGvf0kAyphAUU9IGForsxnS4+FSdEkL8lI0XIosCcnCnNo9t6IDV1/wbAuN2/zxp0dr6QEW9ZaEYUNjNkP62ZJwvJoI+RmQnwX5mX98FWSW/b4oHwz2YGMHBoeSx0YT2LuCg1vJmD8HdzB5gkcAeAaW7COEBZEwFEIIZD1DIYQAJAyFEAKQMBQ1JCkpiXHjxuHv74+dnR1eXl6EhoaybNkycnJqucNGiBogq16KaouLiyM0NBQXFxfmzp1LUFAQtra2nDhxguXLl9OsWTMeeOCBKh9XVVWKi4uxsZG3qagDqhDVNGjQILV58+ZqVlZWuT83m83qmTNnVEA9cuRI6fa0tDQVULdv366qqqpu375dBdTvv/9e7dy5s2owGNTt27erGRkZ6uOPP646ODio3t7e6ttvv63269dPHTduXOmxPv/8c7VLly6qo6Oj6uXlpQ4fPlxNTk4uU0d4eLjarVs31Wg0qt7e3uqUKVPUwsLC0p/369dPHTt2rDp58mTV1dVV9fLyUmfNmlVTL5Oo5+RjsqiWy5cvs2XLFl588UVMJlO5+yiKUqVjTp06lfnz5xMZGUnHjh2ZMGECu3btYtOmTWzdupWdO3dy+PDhMs8pLCxkzpw5HDt2jI0bNxIfH09YWFjpz8+fP8/gwYPp1q0bx44dY9myZfz73//m9ddfL3OclStXYjKZ2LdvH2+++SazZ89m69atVapfWCit01hYtr1796qA+t///rfMdnd3d9VkMqkmk0l95ZVXqtQy3LhxY+k+GRkZqsFgUNetW1e6LT09XXVwcCjTMvyrAwcOqICamZmpqqqqTps2TQ0MDFTNZnPpPu+//77q6OioFhcXq6pa0jLs3bt3meN069ZNnTJlSpVeE2GZpGUoasX+/fs5evQo7du3Jz+/anOmu3btWvo4Li6OwsJCQkJCSrc1atSIwMCyN4A/dOgQQ4YMwcfHBycnJ/r16wdAQkLJXQUjIyPp2bNnmVZqaGgoWVlZnDt3rnRbx44dyxy3SZMmpKTc+F7MwnpIGIpq8ff3R1EUoqOjy2z38/PD398fe/uSmSg6XclbTf3TGP/CwvLvBX2jj9s3kp2dzaBBg3B2duaLL77gwIEDbNiwAYCCghvfY6Y8BkPZhSIURcFsNlfpGMIySRiKanF3d+euu+7ivffeIzs7+4b7eXp6AnDx4h+3MTh69GiFx/fz88NgMHDgwIHSbVevXuXUqVOl30dFRXH58mXmz59Pnz59aNOmzXWtubZt27Jnz54yYbxr1y6cnJxo3lwWixAShqIGfPDBBxQVFdG1a1fWrFlDZGQk0dHRrFq1iqioKPR6Pfb29vTo0aO0Y2THjh3MmDGjwmM7OTnx1FNPMXnyZLZv387JkycZOXIkOp2u9COvj48PRqORpUuXEhcXx6ZNm5gzZ06Z44wePZrExETGjh1LVFQU33zzDbNmzWLChAmlrVbRwGl90VJYhwsXLqhjxoxRW7ZsqRoMBtXR0VENCQlRFy5cqGZnZ6uqqqoRERFqz549VXt7ezU4OFjdsmVLuR0oaWlpZY5d3tCakJAQderUqaX7fPnll6qvr69qa2ur9uzZU920adN1HTaVGVrz106ZBx98UH3qqadq8qUS9ZQs1CAsTnZ2Ns2aNWPRokWMHDlS63KElZCh/aLeO3LkCFFRUYSEhHD16lVmz54NwIMPPqhxZcKaSBgKi/DWW28RHR2N0WikS5cu7Ny5Ew8PD63LElZEPiYLIQTSmyyEEICEoRBCABKGQggBSBgKIQQgYSiEEICEoRBCABKGQggBSBgKIQQgYSiEEICEoRBCABKGQggBSBgKIQQgYSiEEICEoRBCABKGQggBSBgKIQQA/w/V9GFRyBxf/QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(city_values[:5], labels = city_lables[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c394d944b80a5d78f05f49eab34a60570af0846adc11b26ca3fbdbd976f5ba57"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}